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Cited by 371Ethics and Social Impacts of AITechnology's Impact on MediaArtificial Intelligence in Healthcare and Education

The impact of generative artificial intelligence on socioeconomic inequalities and policy making

Open access
Capraro, Valerio, Lentsch, Austin, Acemoglu, Daron, Akgun, Selin, Akhmedova, Aisel, Bilancini, Ennio, Bonnefon, Jean-François, Brañas-Garza, Pablo +25 more·2024·PNAS Nexus
DOI 10.1093/pnasnexus/pgae191PMC PMC11165650
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Abstract

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

AI snapshot

Written from the paper’s full text.

TL;DR

Generative AI can both exacerbate and ameliorate socioeconomic inequalities across information, work, education, and healthcare, with benefits and harms distributed unevenly. The authors argue that current EU, US, and UK policy frameworks insufficiently address these socioeconomic risks and propose concrete policies to promote shared prosperity.

Key findings

  • In information, generative AI can democratize content creation and access but also expands misinformation: manipulated political images already make up ∼20% of visual misinformation on social media, and people are largely unable to distinguish AI- from human-generated text.
  • In work, generative AI shows an “inverse skill-bias”: less-skilled or inexperienced workers gained more. In one Fortune 500 software company study, less-skilled workers resolved around 34% more issues per hour, with average improvement across all workers about 14%; in another study, below-average consultants improved by 43% with AI versus 17% for above-average consultants. Programmers with GitHub Copilot completed a task in 71 min on average versus 161 min for controls.
  • In education, AI offers personalized learning but may widen divides: female students reported using ChatGPT less frequently than male counterparts, and among more than 3,000 US high school students and undergraduates, 96% did not know how to evaluate the trustworthiness of websites.
  • In healthcare, AI may improve diagnostics and reduce bias—one randomized study found GPT-4 advice improved diagnostic accuracy and reduced gender and race bias—but ChatGPT’s answers to Reddit r/AskDocs medical questions were rated higher quality and more empathetic than physicians’ 79% of the time. The authors conclude EU, US, and UK regulations fail to fully confront the socioeconomic challenges identified.

Methods

  • The article is a state-of-the-art interdisciplinary overview, not a primary empirical study. It synthesizes existing research on generative AI’s impacts on (mis)information and three information-intensive domains: work, education, and healthcare.
  • It evaluates existing research, identifies critical gaps, and recommends future research directions, including explicit trade-offs that complicate a priori hypotheses.
  • It also reviews policy approaches in the European Union, the United States, and the United Kingdom and proposes policy recommendations; no systematic review protocol, dataset, or sample is reported.

Limitations

  • The paper states that its research-direction lists are not meant to be exhaustive but serve as an initial guide for subsequent investigations.
  • The paper states that explicit trade-offs complicate the derivation of a priori hypotheses.
  • Evident from the text: as a narrative interdisciplinary overview, it does not use a systematic search or selection method, reports no primary data, and its policy recommendations are proposed for empirical study and public debate rather than tested here.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1093/pnasnexus/pgae191

Cites

122 of 122 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

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  • Interpretationnone given

    New generative AI technology and sophisticated machine learning techniques may enable companies to collect and deploy excessive amounts of information about individuals.

    “Concerningly, new generative AI technology and sophisticated machine learning techniques may also enable companies to collect and deploy excessive amounts of information about individuals.”
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  • Premisecitation

    This type of misinformation can be especially common during elections and intergroup conflicts such as the Russo-Ukrainian and Israel-Gaza wars.

    “This type of misinformation can be especially common during elections and intergroup conflicts such as the Russo-Ukrainian and Israel-Gaza wars (17).”
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    • cites [17] Twomey, 2023, Do deepfake videos undermine our epistemic trust? A thematic analysis of tweets that discuss deepfakes in the Russian invasion of Ukraine, PLoS One source

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    AI may help automate the fact-checking process, aiding the spread of accurate information.

    “AI may also help automate the fact-checking process, aiding the spread of accurate information (7, 8).”
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    • cites [7] Hoes

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    • cites [8] Zavolokina, 2024

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    A dominant model has emerged from these monopolies, where internet platforms earn income by optimally marketing digital advertisements.

    “A dominant model has emerged from these monopolies, where internet platforms earn income by optimally marketing digital advertisements (11).”
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    • cites [11] Acemoglu, 2023

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    The language translation and localization capabilities of AI extend the reach of content globally, crossing traditional language barriers and making material accessible to a wide variety of cultural contexts and social groups.

    “The language translation and localization capabilities of AI extend the reach of content globally, crossing traditional language barriers and making material accessible to a wide variety of cultural contexts and social groups (6).”
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    • cites [6] Rathje

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  • The quote only partly states this claim. 1

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    Findingnone given

    Current regulatory approaches in the European Union, United States, and United Kingdom sometimes fail to adequately address the challenges posed by AI.

    “we have observed that current regulatory approaches in the European Union, United States, and United Kingdom sometimes fail to adequately address these challenges.”
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    The benefits of generative AI in the workplace will likely be distributed unevenly.

    “In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly.”
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    Existing policy frameworks in the European Union, the United States, and the United Kingdom each fail to fully confront the socioeconomic challenges identified.

    “observing that each fails to fully confront the socioeconomic challenges we have identified.”
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    Interpretationcitation

    Technologies like generative AI will likely have negative and positive impacts on society, though they are not inherently predestined toward either.

    “This principle suggests that technologies like generative AI will likely have negative and positive impacts on society, though they are not inherently predestined toward either.”
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    • Rests on an assumption: Both optimists and pessimists agree that generative AI represents a qualitative departure from previous automation processes.

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    In healthcare, generative AI might improve diagnostics and accessibility.

    “In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities.”
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    In education, generative AI may widen the digital divide.

    “In education, it offers personalized learning, but may widen the digital divide.”
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    Generative AI can democratize content creation and access to information.

    “In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation.”
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    Premisecitation

    Advances in generative AI represent a shift in the capability of these systems to solve problems previously thought unsolvable.

    “Advances in generative artificial intelligence (AI) represent a shift in the capability of these systems to solve problems previously thought unsolvable (1).”
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    • cites [1] Bubeck, 2023

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    In the workplace, generative AI can boost productivity and create new jobs.

    “In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly.”
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    Generative AI may dramatically expand the production and proliferation of misinformation.

    “In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation.”
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    In healthcare, generative AI could deepen pre-existing inequalities.

    “In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities.”
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    Technology is neither good nor bad; nor is it neutral.

    ““Technology is neither good nor bad; nor is it neutral” (3).”
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    • cites [3] Kranzberg, 1985, The information age: evolution or revolution, Inform Technol Soc Tansf

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    In education, generative AI offers personalized learning.

    “In education, it offers personalized learning, but may widen the digital divide.”
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    Generative AI has the potential to both exacerbate and ameliorate existing socioeconomic inequalities.

    “Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities.”
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    In education, generative AI promises personalized learning experiences, potentially bridging educational gaps.

    “In education, generative AI promises personalized learning experiences, potentially bridging educational gaps.”
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    Interpretationnone given

    Malicious actors can exploit generative AI to create false information in ways that convincingly copy the style and content of human-created text, by synthetically generating text, audio, images, and videos ("deepfakes").

    “Malicious actors can exploit generative AI to create false information in ways that convincingly copy the style and content of human-created text, by synthetically generating text, audio, images, and videos (“deepfakes”).”
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    There is a risk of generative AI deepening existing inequalities of care and access, especially for under-resourced and marginalized communities.

    “Yet, there is the risk of deepening existing inequalities of care and access, especially for under-resourced and marginalized communities.”
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    The effects of generative AI may eventually expand to virtually every facet of society.

    “The effects of generative AI may eventually expand to virtually every facet of society (5).”
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    • cites [5] Dwivedi, 2023, So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy, Int J Inf Manage. source

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    This strategy places a premium on user attention, which has led companies to deploy AI and machine learning techniques to prolong user engagement, often to the detriment of individual and societal well-being.

    “This strategy places a premium on user attention, which has led companies to deploy AI and machine learning techniques to prolong user engagement, often to the detriment of individual and societal well-being (12–15).”
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    • cites [12] Wu, 2016

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    • cites [13] Acemoglu, 2023, A model of online misinformation, Rev Econ Stud source

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    • cites [14] Acemoglu, 2023

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    • cites [15] Rathje, 2023, People think that social media platforms do (but should not) amplify divisive content, Perspect Psychol Sci source

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    In education, generative AI raises concerns about equal access to advanced tools.

    “However, it also raises concerns about equal access to these advanced tools.”
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    Companies with more data possess an anticompetitive advantage, enabling them to exercise market power to extract surplus and relax price competition, which can be detrimental for consumers.

    “Relatedly, companies with more data possess an anticompetitive advantage, enabling them to exercise market power to extract surplus and relax price competition, which can be detrimental for consumers (9).”
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    • cites [9] Acemoglu, 2024

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    The health sector could greatly benefit from AI's diagnostic and predictive capabilities, improving patient outcomes and making healthcare more accessible.

    “The health sector could greatly benefit from AI's diagnostic and predictive capabilities, improving patient outcomes and making healthcare more accessible.”
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    Generative AI has the potential to revolutionize the information domain, impacting areas such as work, education, healthcare, law, finance, and policy making.

    “Generative AI has the potential to revolutionize the information domain, impacting areas such as work, education, healthcare, law, finance, and policy making.”
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    The benefits and costs of generative AI in the workplace will likely be distributed unevenly across firm sizes, sectors, and worker demographics.

    “However, the benefits and costs will likely be distributed unevenly across firm sizes, sectors, and worker demographics.”
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    In the workplace, generative AI could increase productivity and promote shared prosperity, especially when used to complement human efforts and create new well-paid jobs.

    “In the workplace, generative AI could increase productivity and promote shared prosperity, especially when used to complement human efforts and create new well-paid jobs.”
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    Current policy approaches in the European Union, the United States, and the United Kingdom all fall short in addressing the socioeconomic risks identified.

    “noting that all fall short in addressing the socioeconomic risks that we identify.”
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    This will enable exploitation of consumers' biases or vulnerabilities in order to capture more of the consumer surplus via price discrimination or violations of consumer privacy, leading to "surveillance capitalism".

    “This will enable exploitation of consumers’ biases or vulnerabilities in order to capture more of the consumer surplus via price discrimination or violations of consumer privacy, leading to “surveillance capitalism” (9, 10).”
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    • cites [10] Zuboff, 2023 source

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    Political conspiracy theories and misinformation can affect voting decisions, health-related conspiracy theories can influence people's medical choices (e.g. vaccination), and misinformation and conspiracy theories can fuel conflict between groups.

    “Moreover, political conspiracy theories and misinformation can affect voting decisions, health-related conspiracy theories can influence people's medical choices (e.g. vaccination), and misinformation and conspiracy theories can fuel conflict between groups (29, 30).”
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    • cites [29] Douglas, 2021, Are conspiracy theories harmless?, Span J Psychol. source

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    • cites [30] Sternisko, 2020, The dark side of social movements: social identity, non-conformity, and the lure of conspiracy theories, Curr Opin Psychol. source

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    Conspiracy theories and misinformation can contribute to attitude polarization and undermine trust.

    “Conspiracy theories and misinformation can contribute to attitude polarization (28) and undermine trust (17).”
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    • cites [28] Del Vicario, 2016, The spreading of misinformation online, Proc Natl Acad Sci USA. source

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    • cites [17] Twomey, 2023, Do deepfake videos undermine our epistemic trust? A thematic analysis of tweets that discuss deepfakes in the Russian invasion of Ukraine, PLoS One source

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    AI-assisted misinformation can spread rapidly on social media and micro-targeting people with deepfakes can influence their attitudes toward politicians.

    “AI-assisted misinformation can spread rapidly on social media and micro-targeting people with deepfakes can influence their attitudes toward politicians (e.g. (23)).”
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    • cites [23] Dobber, 2021, Do (microtargeted) deepfakes have real effects on political attitudes?, Int J Press/Politics source

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    Making the concept of accuracy salient can reduce the sharing of fake news, without adversely affecting the dissemination of accurate news.

    “For example, making the concept of accuracy salient can reduce the sharing of fake news, without adversely affecting the dissemination of accurate news (34).”
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    • cites [34] Pennycook, 2022, Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation, Nat Commun. source

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    There is evidence that Trump voters were more susceptible to misinformation during the 2016 presidential election.

    “For example, there is evidence that Trump voters were more susceptible to misinformation during the 2016 presidential election (32).”
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    • cites [32] Grinberg, 2019, Fake news on Twitter during the 2016 U.S. presidential election, Science source

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    While some people simply ignore online misinformation, this content is likely to penetrate specific groups, especially since AI may help automate the micro-targeting process in which thousands of persuasive messages can now be generated easily at scale.

    “While some people simply ignore online misinformation (31), this content is likely to penetrate specific groups, especially since AI may help automate the micro-targeting process in which thousands of persuasive messages can now be generated easily at scale (24).”
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    • cites [31] Acerbi, 2022, Research note: fighting misinformation or fighting for information? Harvard Kennedy School (HKS), Misinform Rev

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    • cites [24] Simchon, 2024, The persuasive effects of political microtargeting in the age of generative artificial intelligence, PNAS Nexus source

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    The 2024 US Presidential election has been dubbed "The Deepfake Election".

    “The 2024 US Presidential election has been dubbed “The Deepfake Election” (27).”
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    • cites [27] Wall Street Journal, 2024, The deepfake election has arrived, J Podcast

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    Interpretationcitation

    In the realm of human-generated misinformation on social media, psychological interventions based on accuracy-salience and educational interventions based on inoculation theory improve the quality of information shared.

    “In the realm of human-generated misinformation on social media, psychological interventions based on accuracy-salience and educational interventions based on inoculation theory improve the quality of information shared.”
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    Some US states have introduced legislation prohibiting the use of deepfakes in political campaigns.

    “Some US states have introduced legislation prohibiting their use (22).”
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    • cites [22] Beller, 2023

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    Endorsing accuracy not only decreases the sharing of false news but also increases the sharing of true news.

    “Moreover, endorsing accuracy not only decreases the sharing of false news but also increases the sharing of true news (35).”
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    • cites [35] Capraro, 2023, I think this news is accurate”: endorsing accuracy decreases the sharing of fake news and increases the sharing of real news, Personal Soc Psychol Bullet source

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    Companies including Microsoft and Google have envisioned integrating large language models into their search engines, but the implications of this move have only started to be explored.

    “Companies including Microsoft and Google have envisioned integrating large language models into their search engines, but the implications of this move have only started to be explored (55).”
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    • cites [55] Cutler, 2023, ChatGPT and search engine optimisation: the future is here, Appl Market Anal

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    While AI can generate responses that make people feel heard by offering emotional support, people feel more heard when they believe a response comes from a human.

    “For instance, while AI can generate responses that make people feel heard by offering emotional support, people feel more heard when they believe a response comes from a human (48).”
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    • cites [48] Yin, 2024, AI can help people feel heard, but an AI label diminishes this impact, Proc Natl Acad Sci USA. source

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    Interpretationreasoning

    In addition to reducing access to information, generative AI may also threaten the quality and availability of online information.

    “In addition to reducing access to information, generative AI may also threaten the quality and availability of online information.”
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    Although users tend to select among the first results returned by the search engine, the interface enables them to browse many alternative results.

    “Although users tend to select among the first results returned by the search engine (56), the interface enables them to browse many alternative results.”
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    • cites [56] Goldman, 2008 source

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    Several inoculation games and videos have been developed to expose subjects to controlled (weakened) doses of misinformation along with tools on how to spot it and these activities make them better at detecting online manipulation.

    “Several inoculation games and videos have been developed to expose subjects to controlled (weakened) doses of misinformation along with tools on how to spot it and these activities make them better at detecting online manipulation (38).”
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    • cites [38] Roozenbeek, 2019, Fake news game confers psychological resistance against online misinformation, Palgrave Commun source

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    When interacting with a machine, people respond less emotionally, feeling less guilt about being ungenerous.

    “When interacting with a machine, people respond less emotionally, feeling less guilt about being ungenerous (52).”
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    • cites [52] Chugunova, 2022, We and it: an interdisciplinary review of the experimental evidence on how humans interact with machines, J Behav Exp Econ source

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    This is increasingly likely to be the case when people engage online with businesses and public services.

    “This is increasingly likely to be the case when people engage online with businesses and public services.”
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    Premisecitation

    Although new technologies can boost productivity, the gains have often fallen below expectations, especially when the focus has been on replacing work instead of augmenting worker capabilities or developing new ones.

    “Although new technologies can boost productivity (69), the gains have often fallen below expectations, especially when the focus has been on replacing work instead of augmenting worker capabilities or developing new ones (11, 70).”
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    • cites [69] Brynjolfsson, 2016

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    • cites [11] Acemoglu, 2023

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    • cites [70] Acemoglu, 2016, Import competition and the great U.S. employment sag of the 2000s, J Labor Econ. source

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    Premisecitation

    Privacy experts have developed the "privacy calculus" to address such trade-offs from the consumer perspective.

    “For instance, privacy experts have developed the “privacy calculus” to address such trade-offs from the consumer perspective (61).”
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    • cites [61] Laufer, 1977, Privacy as a concept and a social issue: a multidimensional developmental theory, J Soc Issues source

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    Wirtz et al. argue that service firms engage in a "corporate digital responsibility calculus" and weigh the benefits and costs of following ethical principles to determine their level of engagement in digital responsibility.

    “Similarly, Wirtz et al. (62) argue that service firms engage in a “corporate digital responsibility calculus” and weigh the benefits and costs of following ethical principles to determine their level of engagement in digital responsibility.”
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    • cites [62] Wirtz, 2023, Corporate digital responsibility in service firms and their ecosystems, J Service Res source

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    Interpretationnone given

    Generative AI has the potential to expand access to and content of information, but also raises significant challenges such as market anticompetitive advantages, data misuse, data poisoning, misinformation proliferation, and altered human-machine interactions.

    “In conclusion, while generative AI has the potential to expand access to and content of information, it also raises significant challenges such as market anticompetitive advantages, data misuse, data poisoning, misinformation proliferation, and altered human–machine interactions, all of which necessitate careful consideration and targeted research.”
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    Interpretationreasoning

    A "pro-worker" or "human-complementary" path could contribute more to productivity growth and could help reduce economic inequality.

    “This “pro-worker” or “human-complementary” path could contribute more to productivity growth and could help reduce economic inequality.”
    Quote in the paperSubstantial claim

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    Premisecitation

    The displacement of labor by "so-so technologies" (e.g. self-checkout kiosks or automated phone systems) that offer little or no productivity gain, along with diminished worker voice due to intensified monitoring and surveillance, can be harmful to long-run productivity and other social goals like job satisfaction.

    “While some amount of this is unavoidable, the displacement of labor by “so-so technologies” (e.g. self-checkout kiosks or automated phone systems) that offer little or no productivity gain, along with diminished worker voice due to intensified monitoring and surveillance, can be harmful to long-run productivity and other social goals like job satisfaction (65, 68).”
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    • cites [65] Acemoglu, 2022, Tasks, automation, and the rise in U.S. wage inequality, Econometrica source

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    • cites [68] Acemoglu, 2019, Automation and new tasks: how technology displaces and reinstates labor, J Econ Perspect source

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  • May combine two claims.

    Premisecitation

    Together, the upside for more-educated workers and downside for less-educated workers have magnified the distributional consequences of technological innovation, highlighting what is sometimes called "Skill-Biased Technological Change".

    “Together, the upside for more-educated workers and downside for less-educated workers have magnified the distributional consequences of technological innovation, highlighting what is sometimes called “Skill-Biased Technological Change” (67).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [67] Acemoglu, 2002, Technical change, inequality, and the labor market, J Econ Lit. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    By adopting responsible practices, corporations can build trust with their customers and stakeholders, differentiate themselves from competitors, and enhance their reputation.

    “By adopting responsible practices, corporations can build trust with their customers and stakeholders, differentiate themselves from competitors, and enhance their reputation.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Current generative AI tools essentially provide an "average" response to a particular question, and these models are trained largely on text data collected from the internet.

    “This is a problem because current generative AI tools essentially provide an “average” response to a particular question, and these models are trained largely on text data collected from the internet”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Common message boards and websites for knowledge sharing (e.g. Stack Overflow) have experienced both a reduction in questions posted-especially the basic questions that ChatGPT does well at answering-and an increase in question responses, perhaps due to writing aid from tools like ChatGPT.

    “An analogous concern is that common message boards and websites for knowledge sharing (e.g. Stack Overflow) have experienced both a reduction in questions posted—especially the basic questions that ChatGPT does well at answering—and an increase in question responses, perhaps due to writing aid from tools like ChatGPT (60).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [60] del Rio-Chanona

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationnone given

    Organizations often face trade-offs between achieving their profitability goals and adhering to ethical practices.

    “Organizations often face trade-offs between achieving their profitability goals and adhering to ethical practices.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    AI can complement workers by making them more efficient, helping them to produce higher quality work, or enabling them to take on new value-adding tasks.

    “There is cause for optimism: AI can complement workers by making them more efficient, helping them to produce higher quality work, or enabling them to take on new value-adding tasks (72–74).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [72] Acemoglu, 2018, The race between man and machine: implications of technology for growth, factor shares, and employment, Am Econ Rev source

      Not checked in the free run.

    • cites [73] Alam, 2024

      Not checked in the free run.

    • cites [74] Korinek, 2023, Generative AI for economic research: use cases and implications for economists, J Econ Lit. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    If the costs of adopting responsible practices is too high, firms may be less likely to engage in good practices and regulatory enforcement may be necessary.

    “However, if the costs of adopting responsible practices is too high, firms may be less likely to engage in good practices and regulatory enforcement may be necessary (62).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [62] Wirtz, 2023, Corporate digital responsibility in service firms and their ecosystems, J Service Res source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Gains from integrating generative AI into medicine could be better targeted to nurses, medical technicians, or healthcare administrators.

    “This suggests that gains from integrating generative AI into medicine could be better targeted to nurses, medical technicians, or healthcare administrators (80).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [80] Acemoglu, 2023

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    The text-production abilities of generative AI present an opportunity to teach students critical thinking.

    “More specifically, the text-production abilities of generative AI present an opportunity to teach students critical thinking.”
    Quote in the paperNot a substantial claim

  • May combine two claims. The quote only partly states this claim.

    Premisecitation

    AI users were more productive and produced higher quality work on complex knowledge-intensive tasks.

    “AI users were more productive and produced higher quality work.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [76] Dell’Acqua, 2023

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    This capability can equalize access to information and lower research costs by simplifying online search tasks.

    “This capability can equalize access to information and lower research costs by simplifying online search tasks.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Generative AI has the ability to parse and aggregate enormous amounts of information.

  • May combine two claims.

    Premisecitation

    Educational uses of generative AI pose several other challenges, one being the perpetuation of biases and discrimination, potentially reinforcing racial or gender-based stereotypes during personalized learning, automated scoring, and admission processes.

    “Educational uses of generative AI pose several other challenges. One is the perpetuation of biases and discrimination, potentially reinforcing racial or gender-based stereotypes during personalized learning, automated scoring, and admission processes (88, 90).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [88] Akgun, 2022, Artificial intelligence in education: addressing ethical challenges in K-12 settings, AI Ethics source

      Not checked in the free run.

    • cites [90] Baker, 2022, Algorithmic bias in education, Int J Artif Intell Educ source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Generative AI will not eliminate the need to learn effective thought organization, writing, and critical thinking skills.

    “In the same vein, generative AI will not eliminate the need to learn effective thought organization, writing, and critical thinking skills.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    If generative AI tools commodify expertise and reduce the returns to specialized skills, workers may no longer spend the time or resources to acquire greater levels of expertise, leading to lower levels of worker skill and over-reliance on outsourcing to generative tools.

    “This effect could compound over time: if generative AI tools commodify expertise and reduce the returns to specialized skills, workers may no longer spend the time or resources to acquire greater levels of expertise, leading to lower levels of worker skill and over-reliance on outsourcing to generative tools.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Using AI systems as clinician copilots could improve diagnostic accuracy and potentially curb biases.

    “Using AI systems as clinician copilots could also improve diagnostic accuracy and potentially curb biases.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Pierson et al. proposed a model, trained on knee X-rays that is nearly five times as effective at predicting knee pain in osteoarthritic patients, compared to the traditional Kellgren-Lawrence grading system, which overlooks racial disparities.

    “Pierson et al. (116) proposed a model, trained on knee X-rays that is nearly five times as effective at predicting knee pain in osteoarthritic patients, compared to the traditional Kellgren–Lawrence grading system, which overlooks racial disparities.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [116] Pierson, 2021, An algorithmic approach to reducing unexplained pain disparities in underserved populations, Nat Med. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The application of AI by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing asymmetric information.

    “Moreover, the application of AI by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing one of the industry's most inherent problems, namely asymmetric information (124).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [124] Eling, 2022, The impact of artificial intelligence along the insurance value chain and on the insurability of risks, Geneva Papers Risk Insurance: Issues Pract source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Generative AI can be used as a conversational companion, potentially replacing some human interactions.

    “Generative AI can be used as a conversational companion, potentially replacing some human interactions.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Generative AI presents significant opportunities to alleviate inequalities in physical and mental health, in addition to augmenting healthcare providers' capabilities.

    “In sum, generative AI presents significant opportunities to alleviate inequalities in physical and mental health, in addition to augmenting healthcare providers’ capabilities.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Humans are social beings, so biological systems can become dysregulated when social needs are unmet, leading to poorer health.

    “Humans are social beings, so our biological systems can become dysregulated when social needs are unmet, leading to poorer health (130).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [130] Beckes, 2022, Social baseline theory: state of the science and new directions, Curr Opin Psychol. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The challenges raised by generative AI are global in nature.

    “The challenges raised by generative AI are global in nature (133).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [133] Jobin, 2019, The global landscape of AI ethics guidelines, Nat Mach Intell source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The European Union's AI Act introduces a risk-based regulatory approach, distinguishing among banned, high-risk, and low-risk AI applications, ensuring stricter scrutiny for high-risk systems.

    “Firstly, it introduces a risk-based regulatory approach, distinguishing among banned, high-risk, and low-risk AI applications. This categorization ensures that AI systems with significant implications for individuals’ rights and safety are subject to stricter scrutiny and compliance requirements.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [136] European Parliament

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    AI could enable insurers to reach currently uninsured groups, reducing inefficiencies and achieving market completeness.

    “AI could also enable insurers to reach currently uninsured groups, reducing inefficiencies and achieving market completeness.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The use of Responsible Artificial Intelligence in healthcare to predict and prevent insurance claim denials could lead to significant cost savings and improved patient well-being.

    “A concrete example of this is the use of Responsible Artificial Intelligence in healthcare to predict and prevent insurance claim denials, which could lead to significant cost savings and improved patient well-being (123).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [123] Johnson, 2021, Responsible artificial intelligence in healthcare: predicting and preventing insurance claim denials for economic and social wellbeing, Inform Syst Front source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Patients penalize and resist the clinical use of AI because they feel they can understand human providers' decision-making as explained, even though human and AI decisions are probably equally unobservable to them.

    “Decisions made by human physicians or AI are probably equally unobservable to a patient—but because patients feel that they can understand the decision-making as explained by human providers, they ultimately penalize and resist the clinical use of AI (122).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [122] Cadario, 2021, Understanding, explaining, and utilizing medical artificial intelligence, Nat Hum Behav source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Insurers could use AI to refine their practices, capturing a larger share of the surplus, which could lead to welfare losses for consumers.

    “Insurers could use AI to refine their practices, capturing a larger share of the surplus. This could lead to welfare losses for consumers.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    Excessive dependence on AI for social engagement could lead to various adverse outcomes, including social isolation and deteriorating mental and physical health.

    “Excessive dependence on AI for social engagement could lead to various adverse outcomes, including social isolation and deteriorating mental and physical health.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    GPT-4 demonstrates reasonable diagnostic accuracy in simple cases and can answer questions on standardized medical exams, though it struggles with diagnostically complex prompts.

    “GPT-4 demonstrates reasonable diagnostic accuracy in simple cases and can answer questions on standardized medical exams, though it struggles with diagnostically complex prompts (107).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [107] Kanjee, 2023, Accuracy of a generative artificial intelligence model in a complex diagnostic challenge, JAMA source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Diagnostic performance of some expert physicians may not be improved by AI-and in fact may cause incorrect diagnoses in situations that otherwise would have been correctly assessed.

    “Alas, diagnostic performance of some expert physicians may not be improved by AI—and in fact may cause incorrect diagnoses in situations that otherwise would have been correctly assessed (79).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [79] Agarwal, 2023

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    There is robust evidence linking social connectedness or lack thereof to long-term health outcomes, including increased risk for chronic illnesses such as cardiovascular disease and stroke, type 2 diabetes, and dementia, as well as mortality from all causes.

    “There is robust evidence linking social connectedness or lack thereof to long-term health outcomes (125), including increased risk for chronic illnesses such as cardiovascular disease and stroke (126), type 2 diabetes, and dementia (127), as well as mortality from all causes (128).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [125] Holt-Lunstad, 2021, The major health implications of social connection, Curr Dir Psychol Sci. source

      Not checked in the free run.

    • cites [126] Valtorta, 2016, Loneliness and social isolation as risk factors for coronary heart disease and stroke: systematic review and meta-analysis of longitudinal observational studies, Heart source

      Not checked in the free run.

    • cites [127] Penninkilampi, 2018, The association between social engagement, loneliness, and risk of dementia: a systematic review and meta-analysis, J Alzheimer's Dis source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The broad definitions and categories within the EU AI Act pose challenges, creating potential uncertainty for AI developers and users.

    “The broad definitions and categories within the Act pose challenges, creating potential uncertainty for AI developers and users.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [137] Morgan

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Creating a more symmetric tax structure would help shift incentives toward human-complementary technological choices by reducing the bias of the tax code toward physical capital over human capital.

    “This would help to shift incentives toward human-complementary technological choices by reducing the bias of the tax code toward physical capital over human capital.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Support for research and development of human-complementary AI technologies could offer strong upsides for growth.

    “Because the current path of research is biased toward automation (11, 68), support for research and development of human-complementary AI technologies could offer strong upsides for growth.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [11] Acemoglu, 2023

      Not checked in the free run.

    • cites [68] Acemoglu, 2019, Automation and new tasks: how technology displaces and reinstates labor, J Econ Perspect source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The EU AI Act promotes ethical AI development, focusing on fundamental rights, nondiscrimination, and privacy.

    “Additionally, the Act promotes ethical AI development, focusing on fundamental rights, nondiscrimination, and privacy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [136] European Parliament

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    The authors believe that the regulations of the European Union, the United States, and the United Kingdom do not pay sufficient attention to socioeconomic inequalities.

    “We believe that the regulations of the European Union, the United States, and the United Kingdom do not pay sufficient attention to socioeconomic inequalities.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The fragmented US approach allows for more flexibility and adaptability in regulation, catering to diverse AI applications and industries, and promotes a more innovation-friendly environment.

    “This approach has its advantages. For one, it allows for more flexibility and adaptability in regulation, catering to the diverse range of AI applications and industries in the United States. It also promotes a more innovation-friendly environment by avoiding overly prescriptive rules that could hinder technological advancement.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The UK White Paper opts against a specialist AI regulator, preferring to support existing regulators in integrating AI considerations.

    “The White Paper opts against a specialist AI regulator, preferring to support existing regulators in integrating AI considerations.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [140] Department for Science, Innovation, and Technology

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Without a cohesive national strategy, the United States risks falling behind in setting global standards for AI governance.

    “Further, without a cohesive national strategy, the United States risks falling behind in setting global standards for AI governance.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The lack of a unified US regulatory framework can lead to inconsistencies and uncertainties, creating a complex patchwork of regulations for AI companies to navigate, and might lag in addressing broader ethical and social concerns about AI.

    “However, it also has notable disadvantages. The lack of a unified regulatory framework can lead to inconsistencies and uncertainties, potentially creating a complex patchwork of regulations for AI companies to navigate. This fragmented approach might also lag in addressing broader ethical and social concerns about AI, such as privacy, bias, and accountability.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    This tax structure has resulted in a lower share of income to labor while capital investments are rewarded.

    “This has resulted in a lower share of income to labor while capital investments are rewarded.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The UK White Paper outlines cross-sectoral nonstatutory soft principles: safety, security, robustness, appropriate transparency and explainability, fairness, accountability and governance, and contestability and redress.

    “Instead, the White Paper outlines some “cross-sectoral” nonstatutory soft principles: safety, security, robustness, appropriate transparency and explainability, fairness, accountability and governance, and contestability and redress.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [140] Department for Science, Innovation, and Technology

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    Generative AI holds enormous potential to provide policy suggestions, due to its capacity to analyze vast amounts of data, recognize complex patterns, and offer insights that might elude human analysis.

    “Generative AI holds enormous potential to provide policy suggestions, due to its capacity to analyze vast amounts of data, recognize complex patterns, and offer insights that might elude human analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    The effects of generative AI will ultimately depend on the choices that we make to design and deploy the technology.

    “But the effects of generative AI will ultimately depend on the choices that we make to design and deploy the technology.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Chatbot behavior is similar to human behavior, suggesting that GPT could have the potential in assisting human decision-making.

    “In synthesis, these findings suggest that chatbots behavior is similar to human behavior, leading to the suggestion that “GPT could have the potential in assisting human decision-making” (147).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [147] Chen, 2023, The emergence of economic rationality of GPT, Proc Natl Acad Sci USA. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Generative AI also poses several challenges, including anticompetitive market advantages, data misuse and abuse, and misinformation.

    “Yet it also poses several challenges, including anticompetitive market advantages, data misuse and abuse, and misinformation.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    There is no consensus amongst laypeople about how moral dilemmas should be resolved, nor is there normative agreement amongst philosophers on how they should be resolved and why.

    “There is no consensus amongst laypeople about how such moral dilemmas should be resolved (154, 155), nor is there normative agreement amongst philosophers on how they should be resolved and why.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [154] Greene, 2001, An fMRI investigation of emotional engagement in moral judgment, Science source

      Not checked in the free run.

    • cites [155] Kahane, 2018, Beyond sacrificial harm: a two-dimensional model of utilitarian psychology, Psychol Rev. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    GPT-4 consistently underestimates people's self-interest and inequity-aversion, while overestimating their level of altruistic behavior.

    “While this research highlights the ability of large language models to simulate human behavior, it has also been observed that GPT-4 consistently underestimates people's self-interest and inequity-aversion, while overestimating their level of altruistic behavior (150).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [150] Capraro

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    This technology has profound implications in the domain of information, where it has the potential to offer more tailored, efficient, and democratic ways to process information.

    “This technology has profound implications in the domain of information, where it has the potential to offer more tailored, efficient, and democratic ways to process information.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    The ethical and practical concerns of using AI for policymaking are significant and perhaps even prohibitive with the current tools available.

    “However, the ethical and practical concerns of using AI for policymaking are significant and perhaps even prohibitive with the current tools available.”
    Quote in the paperNot a substantial claim
99 of 99

  • The paper relies on this without support.

    Both optimists and pessimists agree that generative AI represents a qualitative departure from previous automation processes.

    “Both optimists and pessimists agree that generative AI represents a qualitative departure from previous automation processes, such as microelectronics, information technology, and the Internet.”

  • The paper relies on this without support.

    Generative AI introduces the ability to create new, original output.

    “Unlike traditional automation, which primarily focuses on replicating predefined tasks, generative AI introduces the ability to create new, original output.”

  • The paper relies on this without support.

    AI can tailor content to individual preferences, enhancing and customizing user experiences.

    “One advantage is the ability for personalization, where AI can tailor content to individual preferences, enhancing and customizing user experiences.”

  • The paper relies on this without support.

    AI aids in making information more accessible for individuals with disabilities, by creating text-alternative formats like audio or simplified summaries.

    “For example, AI aids in making information more accessible for individuals with disabilities, by creating text-alternative formats like audio or simplified summaries.”

  • The paper relies on this without support.

    Malicious generative AI tools like WormGPT or PoisonGPT show that these tools can be used to accomplish malign aims and to sabotage further technology development.

    “For instance, malicious generative AI tools like WormGPT (a ChatGPT alternative for designing and refining cyber-attack strategies and malware) or PoisonGPT (a modified open-source AI model designed to spread misinformation) show that these tools can be used to accomplish malign aims and to sabotage further technology development.”

  • The paper relies on this without support.

    There is currently little legislation preventing the use of deepfakes in political campaigns.

    “There is currently little legislation preventing the use of deepfakes in political campaigns, although there are some steps in this direction.”

  • The paper relies on this without support.

    The EU AI Act (Article 52(3)) does not outlaw deepfakes, but at least requires platforms to identify AI-generated content.

    “The EU AI Act (Article 52(3)) does not outlaw deepfakes, but at least requires platforms to identify AI-generated content.”

  • The paper relies on this without support.

    When users input a query into the current version of Google Search they are pointed to a plurality of sources.

    “When users input a query into the current version of Google Search they are pointed to a plurality of sources.”

  • The paper relies on this without support.

    Generative AI has the ability to parse and aggregate enormous amounts of information.

    “One strength of generative AI is its ability to parse and aggregate enormous amounts of information.”

  • The paper relies on this without support.

    The most widely available and accessible generative AI platforms still require additional technical inputs (e.g. internet access and internet-enabled devices) as well as training to optimize performance.

    “The most widely available and accessible generative AI platforms still require additional technical inputs (e.g. internet access and internet-enabled devices) as well as training to optimize performance.”

  • The paper relies on this without support.

    These benefits have mostly favored well developed countries.

    “Yet, these benefits have mostly favored well developed countries.”

  • The paper relies on this without support.

    Human educators are susceptible to biases and discrimination.

    “Given that human educators are susceptible to biases and discrimination, AI systems offer a theoretical advantage: they could be engineered to exhibit less bias.”

  • The paper relies on this without support.

    The output of generative AI is much more variable than other educational technologies (e.g. calculators).

    “The output of generative AI is much more variable than other educational technologies (e.g. calculators); therefore, developing these critical thinking abilities and prompt-engineering skills is fundamental.”

  • The paper relies on this without support.

    Fact-checking skills are not taught sufficiently in schools.

    “Fact-checking skills are not taught sufficiently in schools.”

  • The paper relies on this without support.

    Large language models are statistical machines that calculate correlations between words.

    “Lastly, understanding the nature of large language models, which are statistical machines that calculate correlations between words, is essential.”

  • The paper relies on this without support.

    Being part of a stigmatized group affects people's engagement and utilization of healthcare services.

    “Being part of a stigmatized group affects people's engagement and utilization of healthcare services.”

  • The paper relies on this without support.

    Pre-existing societal biases are often baked into healthcare data—especially when that data consist of human clinicians’ decisions and the socioeconomic factors that influence patients’ presentation to healthcare facilities.

    “Pre-existing societal biases are often baked into healthcare data—especially when that data consist of human clinicians’ decisions and the socioeconomic factors that influence patients’ presentation to healthcare facilities.”

  • The paper relies on this without support.

    Many of the diagnostic criteria and treatment algorithms used in healthcare are also subject to bias, which can drive unequal health outcomes for underserved populations.

    “Furthermore, many of the diagnostic criteria and treatment algorithms used in healthcare are also subject to bias, which can drive unequal health outcomes for underserved populations.”

  • The paper relies on this without support.

    Hospitals are developing siloed versions of generative models to facilitate care while preserving privacy.

    “While hospitals are developing siloed versions of generative models to facilitate care while preserving privacy, the true potential of AI models will likely rely on methods that leverage the value of large, generalizable datasets.”

  • The paper relies on this without support.

    Today, it is not possible to determine highly accurate, individualized probabilities for the future health conditions of a particular insurant; insurance relies instead on population-level probabilities with some refinement from explicit risk factors.

    “Today, it is not possible to determine highly accurate, individualized probabilities for the future health conditions of a particular insurant—insurance as a field relies instead on population-level probabilities, with some refinement from explicit risk factors.”

  • The paper relies on this without support.

    AI will have tremendous impact across industries and throughout society.

    “Given that AI will have tremendous impact across industries and throughout society, it would be prudent to ensure that workers and civil society have a voice in this change.”

  • The paper relies on this without support.

    A handful of very large companies will control the direction of generative AI.

    “Given the concerns that a handful of very large companies will control the direction of generative AI, it is reasonable that users be compensated for the use of their information, or enabled to support other emergent competitors to predominant market players like Microsoft and Google.”

  • The paper relies on this without support.

    Generative AI can have a substantial impact on misinformation.

    “Given the substantial impact that generative AI can have on misinformation, it is critical for governments to invest in combating AI-generated misinformation.”

  • The paper relies on this without support.

    AI will touch every area of government investment, regulation, and oversight.

    “AI will touch every area of government investment, regulation, and oversight.”

  • The paper relies on this without support.

    AI technology is advancing rapidly.

    “keep pace with the rapid advancements in AI technology”
25 of 25

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Research works printed but not linked: 10

  1. Bostrom, 2017, Superintelligence
  2. Kranzberg, 1985, The information age: evolution or revolution, Inform Technol Soc Tansf, 35
  3. Acemoglu, 2024, The Oxford handbook of AI governance, forthcoming
  4. Acemoglu, 2023, Power and progress: our 1000-year struggle over technology and prosperity
  5. Wall Street Journal, 2024, The deepfake election has arrived, J Podcast
  6. Acerbi, 2022, Research note: fighting misinformation or fighting for information? Harvard Kennedy School (HKS), Misinform Rev, 3
  7. Van der Linden, 2023, Foolproof: why misinformation infects our minds and how to build immunity
  8. Ferrara, 2016, The rise of social bots, Commun Assoc Comput Mach, 59, 96
  9. Ehrenfeld, 2023, AMA President sounds alarm on national physician shortage
  10. Pasquale, 2020, New laws of robotics

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Authors
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., Everett, J. A. C., Gigerenzer, G., Greenhow, C., Hashimoto, D. A., Holt-Lunstad, J., Jetten, J., Johnson, S., Kunz, W. H., Longoni, C., Lunn, P., Natale, S., Paluch, S., Rahwan, I., Selwyn, N., Singh, V., Suri, S., Sutcliffe, J., Tomlinson, J., van der Linden, S., Van Lange, P. A. M., Wall, F., Van Bavel, J. J., & Viale, R.
Affiliations
  • University of Milano-Bicocca (IT)
  • Massachusetts Institute of Technology (US)
  • Institute Professor and Department of Economics, MIT , Cambridge, MA 02142 , USA
  • College of Education, Michigan State University, East Lansing, MI 48824, USA
  • Michigan State University (US)
  • IMT School for Advanced Studies Lucca (IT)
  • Toulouse School of Economics (FR)
  • , Loyola Andalucia University Loyola Behavioral Lab , Córdoba 41740, Spain
  • Copenhagen Business School (DK)
  • University of Kent (GB)
  • Max Planck Institute for Human Development (DE)
  • Department of Psychology and Neuroscience, Brigham Young University , Provo, UT 84602 , USA
  • The University of Queensland (AU)
  • University of Massachusetts Boston (US)
  • Bocconi University (IT)
  • Economic and Social Research Institute (IE)
  • University of Turin (IT)
  • Aarhus University (DK)
  • Monash University (AU)
  • University of Pennsylvania (US)
  • Microsoft (United States) (US)
  • University of York (GB)
  • Department of Psychology, University of Cambridge , Cambridge CB 21TN, UK
  • Department of Experimental and Applied Psychology, Vrije Universiteit , Amsterdam 1081HV , The Netherlands
  • University of Klagenfurt (AT)
  • Department of Psychology & Center for Neural Science, New York University , New York, NY 10012, USA
  • Laboratoire des applications Thérapeutiques des Ultrasons (FR)
Corresponding author
Capraro, Valerio
ORCID
Venue
PNAS Nexus
Publisher
Oxford University Press (OUP)
Published
2024-05-31
Volume
3
Issue
6
Pages
pgae191-pgae191
ISSN
2752-6542 (ISSN-L)
DOI
10.1093/pnasnexus/pgae191
PMC
PMC11165650
OpenAlex
W4399518864
Semantic Scholar
30c5c481ded2686e21ce108925d4ecf9d0c62d05
Cited by
371
References
122
Citations by year
2026: 167 · 2025: 165 · 2024: 36 · 2023: 3
FWCI
28.01
Type
journal article
Language
en
Open access
Licence
Locations
  • PNAS Nexus (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • VU Research Portal (publishedVersion, other-oa)
  • Monash University Research Portal (Monash University) (acceptedVersion, other-oa)
  • Kent Academic Repository (University of Kent) (acceptedVersion)
  • Toulouse 1 Capitole Publications (Université Toulouse I Capitole) (acceptedVersion)
  • HAL (submittedVersion, cc-by)
  • Archivio istituzionale della Ricerca - Bocconi (submittedVersion, cc-by)
  • Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
  • BOA (University of Milano-Bicocca) (submittedVersion, other-oa)
  • Research Portal (King's College London) (submittedVersion, cc-by)
  • CBS Research Portal (Copenhagen Business School) (submittedVersion, cc-by)
  • RWTH Publications (RWTH Aachen)
Funders
  • Hewlett Foundation
  • NSF
  • Schmidt Sciences
  • Google
  • Sloan Foundation
  • Smith Richardson Foundation
  • Washington Center for Equitable Growth (ANR-19-PI3A-0004, ANR-17-EURE-0010)
  • Spanish Ministry of Science and Innovation (PID2021-126892NB-I00)
  • European Research Council (101018262)
  • ESRC (ES/V015176/1)
  • Leverhulme Trust (PLP-2021-095)
  • Australian Research Council (FL180100094)
  • Sloan School
  • MIT
  • University of Turin (NATS_GFI_22_01_F)
  • Ammodo science award
  • Royal Netherlands Academy of Arts and Sciences
  • Google Jigsaw
  • Center for Conflict and Cooperation
  • Templeton World Charity Foundation (TWCF-2022-30561)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology's Impact on Media
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI, socioeconomic disparities, misinformation, digital divide, labor productivity, job creation, personalized learning, health care disparities, AI policy, EU AI policy, United States AI policy, UK AI policy, shared prosperity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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